initial commit

app.py

@@ -0,0 +1,72 @@
+import cv2
+import gradio as gr
+import numpy as np
+from paddleocr import PaddleOCR
+from PIL import Image
+from transformers import pipeline
+from transformers.pipelines.document_question_answering import apply_tesseract
+
+PIPE = pipeline("document-question-answering", "impira/layoutlm-document-qa")
+OCR = PaddleOCR(
+    use_angle_cls=True,
+    lang="en",
+    det_limit_side_len=10_000,
+    det_db_score_mode="slow",
+    enable_mlkdnn=True,
+)
+
+
+PADDLE_OCR_LABEL = "PaddleOCR (en)"
+TESSERACT_LABEL = "Tesseract (HF default)"
+
+
+def predict(image: Image.Image, question: str, ocr_engine: str):
+    image_np = np.asarray(image)
+
+    if ocr_engine == PADDLE_OCR_LABEL:
+        ocr_result = OCR.ocr(image_np)[0]
+        words = [x[1][0] for x in ocr_result]
+        boxes = np.asarray([x[0] for x in ocr_result])  # (n_boxes, 4, 2)
+
+        for box in boxes:
+            cv2.polylines(image_np, [box.reshape(-1, 1, 2).astype(int)], True, (0, 255, 255), 3)
+
+        x1 = boxes[:, :, 0].min(1) * 1000 / image.width
+        y1 = boxes[:, :, 1].min(1) * 1000 / image.height
+        x2 = boxes[:, :, 0].max(1) * 1000 / image.width
+        y2 = boxes[:, :, 1].max(1) * 1000 / image.height
+
+        # (n_boxes, 4) in xyxy format
+        boxes = np.stack([x1, y1, x2, y2], axis=1).astype(int)
+
+    elif ocr_engine == TESSERACT_LABEL:
+        words, boxes = apply_tesseract(image, None, "")
+
+        for x1, y1, x2, y2 in boxes:
+            x1 = int(x1 * image.width / 1000)
+            y1 = int(y1 * image.height / 1000)
+            x2 = int(x2 * image.width / 1000)
+            y2 = int(y2 * image.height / 1000)
+            cv2.rectangle(image_np, (x1, y1), (x2, y2), (0, 255, 255), 3)
+
+    else:
+        raise ValueError(f"Unsupported ocr_engine={ocr_engine}")
+
+    word_boxes = list(zip(words, boxes))
+    result = PIPE(image, question, word_boxes)[0]
+    return result["answer"], result["score"], image_np
+
+
+gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        "text",
+        gr.Radio([PADDLE_OCR_LABEL, TESSERACT_LABEL]),
+    ],
+    outputs=[
+        gr.Textbox(label="Answer"),
+        gr.Number(label="Score"),
+        gr.Image(label="OCR results"),
+    ],
+).launch()

packages.txt

@@ -0,0 +1 @@
+tesseract-ocr5

requirements.txt

@@ -0,0 +1,8 @@
+--extra-index-url https://mirror.baidu.com/pypi/simple
+numpy
+torch
+transformers
+paddlepaddle==2.5.1
+paddleocr
+opencv-python-headless
+pytesseract